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Introduction Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown. Methods Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs. Results In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment. Conclusions Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs. Highlights While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use. Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19. Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.

The Predictive Approaches to Treatment Effect Heterogeneity statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in the RCT setting. The aim of this study was to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: 1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; 2) identification of relevant databases; 3) development of a prediction model for the outcome(s) of interest; 4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; 5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of angiotensin-converting enzyme (ACE) inhibitors versus beta blockers on three efficacy and six safety outcomes across three observational databases. The proposed framework can supplement any comparative effectiveness study. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction received negligible absolute benefits for all three efficacy outcomes, though they were more pronounced in the highest risk quarter, especially for hospitalization with heart failure. However, failing diagnostics showed evidence of residual imbalances even after adjustment for observed confounding. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.

Background Creatinine clearance (CrCl) is an independent determinant of mortality in predictive models of revascularisation outcomes for complex coronary artery disease. Aims This study aimed to investigate the impact of preprocedural biological markers on 10-year mortality following coronary revascularisation. Methods The SYNTAX Extended Survival (SYNTAXES) study evaluated the 10-year vital status follow-up of 1,800 patients with de novo three-vessel (3VD) and/or left main coronary artery disease (LMCAD) randomised to include percutaneous or surgical coronary revascularisation. The associations between mortality and preprocedural C-reactive protein (CRP), haemoglobin, HbA1c, CrCl, fasting triglycerides, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol were analysed. Results Out of 1,800 patients, 460 patients died before the 10-year follow-up. CRP, HbA1c and CrCl with threshold values of ≥2 mg/L, ≥6% (42 mmol/mol) and l60 ml/min, respectively, were associated with 10-year all-cause death (adjusted hazard ratio [95% confidence interval]: 1.35 [1.01-1.82], 1.51 [1.16-1.95], and 1.46 [1.07-2.00], respectively). There was no significant interaction between the biological markers on all-cause mortality and the type of revascularisation. Preprocedural lipid markers were not significantly associated with 10-year all-cause death, but the non-use of statins was a determinant factor of worse prognosis (adjusted confidence interval [95% confidence interval]: 1.68 [1.26-2.25]). Conclusions Preprocedural biomarkers, such as CRP and HbA1c, are associated with long-term mortality post revascularisation, regardless of the revascularisation technique. Conventional lipidic biomarkers associated with high-risk of cardiovascular events seem to be effectively mitigated by the long-term use of statins, whereas the non-use of statins was a factor of a worse prognosis, emphasising the importance of pharmacological treatment. Trial registration SYNTAXES ClinicalTrials.gov reference: NCT03417050. SYNTAX ClinicalTrials.gov reference: NCT00114972.

Purpose: Numerous studies have modified the Trauma Injury and Severity Score (TRISS) to improve its predictive accuracy for specific trauma populations. The aim of this study was to develop and validate a simple and practical prediction model that accurately predicts mortality for all acute trauma admissions. Methods: This retrospective study used Dutch National Trauma Registry data recorded between 2015 and 2018. New models were developed based on nonlinear transformations of TRISS variables (age, systolic blood pressure (SBP), Glasgow Coma Score (GCS) and Injury Severity Score (ISS)), the New Injury Severity Score (NISS), the sex–age interaction, the best motor response (BMR) and the American Society of Anesthesiologists (ASA) physical status classification. The models were validated in 2018 data and for specific patient subgroups. The models’ performance was assessed based on discrimination (areas under the curve (AUCs)) and by calibration plots. Multiple imputation was applied to account for missing values. Results: The mortality rates in the development and validation datasets were 2.3% (5709/245363) and 2.5% (1959/77343), respectively. A model with sex, ASA class, and nonlinear transformations of age, SBP, the ISS and the BMR showed significantly better discrimination than the TRISS (AUC 0.915 vs. 0.861). This model was well calibrated and demonstrated good discrimination in different subsets of patients, including isolated hip fractures patients (AUC: 0.796), elderly (AUC: 0.835), less severely injured (ISS16) (AUC: 878), severely injured (ISS ≥ 16) (AUC: 0.889), traumatic brain injury (AUC: 0.910). Moreover, discrimination for patients admitted to the intensive care (AUC: s0.846), and for both non-major and major trauma center patients was excellent, with AUCs of 0.940 and 0.895, respectively. Conclusion: This study presents a simple and practical mortality prediction model that performed well for important subgroups of patients as well as for the heterogeneous population of all acute trauma admissions in the Netherlands. Because this model includes widely available predictors, it can also be used for international evaluations of trauma care within institutions and trauma systems.

Contains fulltext : 293513.pdf (Publisher’s version ) (Open Access) BACKGROUND: Loss of life expectancy (LOLE) may provide more intuitive information on the impact of cancer than relative survival over a fixed time horizon (e.g., 5-year relative survival). We aimed to assess the evolution of the LOLE using a nationwide, population-based cohort including patients diagnosed with one of 17 most frequent solid malignancies. METHODS: From the Netherlands Cancer Registry, we selected adult patients diagnosed with one of the 17 most frequent solid malignancies in the Netherlands during 1989-2019, with survival follow-up until 2022. We used flexible parametric survival models to estimate the LOLE at diagnosis and the LOLE after surviving several years post-diagnosis (conditional LOLE; CLOLE) by cancer type, calendar year, age, sex, and disease stage. FINDINGS: For all cancers combined, the LOLE consistently decreased from 1989 to 2019. This decrease was most pronounced for males with prostate cancer (e.g., from 6.9 [95% confidence interval [CI], 6.7-7.1] to 2.7 [95% CI, 2.5-3.0] for 65-year-olds) and females with breast cancer (e.g., from 6.6 [95% CI, 6.4-6.7] to 1.9 [95% CI, 1.8-2.0] for 65-year-olds). The LOLE among patients with cancers of the head and neck or the central nervous system remained constant over time. Overall, the CLOLE showed that the life years lost among patients with cancer decreased with each additional year survived post-diagnosis. For example, the LOLE at diagnosis for 65-year-old females diagnosed with breast cancer in 2019 was 1.9 [95% CI, 1.8-2.0] compared with 1.7 [95% CI, 1.6-1.8], 1.0 [95% CI, 0.9-1.1], and 0.5 [95% CI, 0.5-0.6] when surviving one, five, and ten years post-diagnosis, respectively. Estimates for other combinations of patient and tumour characteristics are available in a publicly available web-based application. INTERPRETATION: Our findings suggested that the evolution of LOLE substantially varies across cancer type, age, and disease stage. LOLE estimates help patients better understand the impact of their specific cancer diagnosis on their life expectancy. FUNDING: None. 01 juni 2023

For esophageal cancer patients, detection of residual tumor after neoadjuvant chemoradiotherapy (nCRT) is important for guiding treatment decisions regarding standard esophagectomy or active surveillance. Prediction models incorporating clinical variables and FDG-PET quantitative radiomic features have been previously developed in a single-center setting (‘development cohort’, Beukinga et al. 2018) to identify patients with residual tumor after nCRT. The aim was to improve generalizability by redeveloping the models (i.e. ‘model extension’) using a multicenter prospectively acquired cohort. The development cohort was combined with a patient cohort collected from a prospective multicenter study in four Dutch institutes. Patients underwent nCRT according to the CROSS regimen followed by esophagectomy 6–14 weeks after nCRT between 2013–2019. Outcome was tumor regression grade (TRG) 1–2 (0–10% tumor) vs TRG 3–4 (>10% tumor). FDG-PET/CT was performed 6–12 weeks after nCRT. Post-treatment FDG-PET-radiomic features were calculated from gross tumor volumes. Features were standardized per scanner. A bootstrapped least absolute shrinkage and selection (LASSO) model using clinical variables and radiomic features was developed and a model incorporating clinical variables was developed for reference. Some 262 patients were included. Median age was 65 years (IQR 60–70), 158 of 262 were male (82%) and outcomes were 129/262 TRG 1–2 (49%) and 133/262 TRG 3–4 (51%). The extended LASSO model included seven radiomic features and the clinical variables cT stage and histology (internally validated AUC, 0.59). At a probability threshold to obtain 90% sensitivity for detection of TRG 3–4 (benchmark with bite-on-bite biopsies + fine-needle aspiration of suspected lymph nodes), specificity was 36% and accuracy 63%. A LASSO clinical reference model (including cT, sex, histology) achieved an internally validated AUC of 0.58 (Fig. 1). The extended internally validated LASSO model incorporating post-treatment radiomic features and clinical variables yielded moderate discriminative ability. The improvement in predictive performance with this model compared with a model using only clinical variables was marginal. The application of radiomics to post-treatment FDG-PET/CT scans is of no help in decision-making in individual patients regarding the choice for active surveillance after nCRT.

Rationale and research objective: As a result of neoadjuvant chemotherapy (NAC), approximately 1/3 of clinically node positive (cN+) breast cancer patients convert to a pathologic complete response (pCR) of the axilla. Targeted Axillary Dissection appears to be most accurate to evaluate axillary treatment-response, but evidence to support this is limited. The RISAS trial was set up to determine the accuracy of sentinel lymph node biopsy (SLNB) combined with excision of the pretreatment marked positive lymph node for axillary staging after NAC in cN+ patients. Methods: This prospective, multicenter trial included cT1-4N1,2,3b patients treated with NAC. Prior to NAC, an iodine seed was placed in the pathologically confirmed positive lymph node. After NAC, all patients underwent SLNB together with excision of the marked lymph node (RISAS-procedure) followed by axillary lymph node dissection (ALND). The identification rate and the accuracy (i.e. false negative rate (FNR) and negative predictive value (NPV)) were calculated for the RISAS procedure. Assuming an FNR of 2%, a prevalence of a positive ALND of 64% and a 10% drop-out rate, a sample size of 248 patients was needed to determine non-inferiority. The NULL hypothesis of inferiority would be rejected at a significance level of 5% if the upper bound of the two-sided 90% Clopper-Pearson confidence interval of the observed FNR would be below the non-inferiority margin of 6.25%. Secondary objectives included the accuracy of the SLNB and excision of the marked lymph node separately. Results: A total of 252 pathologically proven cN+ patients provided informed consent of whom 227 underwent the RISAS procedure/Targeted Axillary Dissection (SLNB combined with excision of the marked positive lymph node). The identification rate of the RISAS procedure is 98% (223/227). Preliminary analysis shows a false negative rate of approximately 5% and a negative predictive value of approximately 91%. Furthermore, by combining SLNB with excision of the marked lymph node, both the identification rate and the accuracy are improved compared to either the SLNB alone or the excision of the marked lymph node alone. Conclusion: This prospective multicenter validation trial shows that the RISAS procedure/Targeted Axillary Dissection is most suitable in terms of identification rate and accuracy to replace ALND for axillary staging after NAC in cN+ patients. The trial was funded by the Dutch Cancer Society (KWF, grant number 2015-8023). Citation Format: Janine Simons, Thiemo JA v Nijnatten, Linetta B Koppert, Carmen C van der Pol, Paul J v Diest, Agnes Jager, David v Klaveren, Boen LR Kam, Marc BI Lobbes, Maaike de Boer, Cees Verhoef, Paul RA Sars, Harold J Heijmans, Els RM van Haaren, Wouter J Vles, Caroline ME Contant, Marian BE Menke, Leonie HM Smit, Wendy Kelder, Marike Boskamp, Ingeborg Mares-Engelberts, Ernest JT Luiten, Marjolein L Smidt. Radioactive Iodine Seed placement in the Axilla with Sentinel lymph node biopsy after neoadjuvant chemotherapy in breast cancer: Results of the prospective multicenter RISAS trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS1-10.

Background This study was conducted to validate a pretreatment (i.e. prior to neoadjuvant chemoradiotherapy) pathological staging system in the resection specimen after neoadjuvant chemoradiotherapy for esophageal cancer. The study investigated the prognostic value of pretreatment pathological T and N categories (prepT and prepN categories) in both an independent and a combined patient cohort. Methods Patients with esophageal cancer treated with neoadjuvant chemotherapy and esophagectomy between 2012 and 2015 were included. PrepT and prepN categories were estimated based on the extent of tumor regression and regressional changes of lymph nodes in the resection specimen. The difference in Akaike’s information criterion (ΔAIC) was used to assess prognostic performance. PrepN and ypN categories were combined to determine the effect of nodal sterilization on prognosis. A multivariable Cox regression model was used to identify combined prepN and ypN categories as independent prognostic factors. Results The prognostic strength of the prepT category was better than the cT and ypT categories (ΔAIC 7.7 vs. 3.0 and 2.9, respectively), and the prognostic strength of the prepN category was better than the cN category and similar to the ypN category (ΔAIC 29.2 vs. − 1.0 and 27.9, respectively). PrepN + patients who became ypN0 had significantly worse survival than prepN0 patients (2-year overall survival 69% vs. 86% in 137 patients; p = 0.044). Similar results were found in a combined cohort of 317 patients (2-year overall survival 62% vs. 85%; p = 0.002). Combined prepN/ypN stage was independently associated with overall survival. Conclusions These results independently confirm the prognostic value of prepTNM staging. PrepTNM staging is of additional prognostic value to cTNM and ypTNM. PrepN0/ypN0 patients have a better survival than prepN +/ypN0 patients.

The Platelets for Neonatal Thrombocytopenia (PlaNeT-2) trial reported an unexpected overall benefit of a prophylactic platelet transfusion threshold of 25 × 109/L compared with 50 × 109/L for major bleeding and/or mortality in preterm neonates (7% absolute-risk reduction). However, some neonates in the trial may have experienced little benefit or even harm from the 25 × 109/L threshold. We wanted to assess this heterogeneity of treatment effect in the PlaNet-2 trial, to investigate whether all preterm neonates benefit from the low threshold. We developed a multivariate logistic regression model in the PlaNet-2 data to predict baseline risk of major bleeding and/or mortality for all 653 neonates. We then ranked the neonates based on their predicted baseline risk and categorized them into 4 risk quartiles. Within these quartiles, we assessed absolute-risk difference between the 50 × 109/L- and 25 × 109/L-threshold groups. A total of 146 neonates died or developed major bleeding. The internally validated C-statistic of the model was 0.63 (95% confidence interval, 0.58-0.68). The 25 × 109/L threshold was associated with absolute-risk reduction in all risk groups, varying from 4.9% in the lowest risk group to 12.3% in the highest risk group. These results suggest that a 25 × 109/L prophylactic platelet count threshold can be adopted in all preterm neonates, irrespective of predicted baseline outcome risk. Future studies are needed to improve the predictive accuracy of the baseline risk model. This trial was registered at www.isrctn.com as #ISRCTN87736839.

Numerous studies have modified the Trauma Injury and Severity Score (TRISS) to improve its predictive accuracy for specific trauma populations. The aim of this study was to develop and validate a simple and practical prediction model that accurately predicts mortality for all acute trauma admissions.This retrospective study used Dutch National Trauma Registry data recorded between 2015 and 2018. New models were developed based on nonlinear transformations of TRISS variables (age, systolic blood pressure (SBP), Glasgow Coma Score (GCS) and Injury Severity Score (ISS)), the New Injury Severity Score (NISS), the sex-age interaction, the best motor response (BMR) and the American Society of Anesthesiologists (ASA) physical status classification. The models were validated in 2018 data and for specific patient subgroups. The models' performance was assessed based on discrimination (areas under the curve (AUCs)) and by calibration plots. Multiple imputation was applied to account for missing values.The mortality rates in the development and validation datasets were 2.3% (5709/245363) and 2.5% (1959/77343), respectively. A model with sex, ASA class, and nonlinear transformations of age, SBP, the ISS and the BMR showed significantly better discrimination than the TRISS (AUC 0.915 vs. 0.861). This model was well calibrated and demonstrated good discrimination in different subsets of patients, including isolated hip fractures patients (AUC: 0.796), elderly (AUC: 0.835), less severely injured (ISS16) (AUC: 878), severely injured (ISS ≥ 16) (AUC: 0.889), traumatic brain injury (AUC: 0.910). Moreover, discrimination for patients admitted to the intensive care (AUC: s0.846), and for both non-major and major trauma center patients was excellent, with AUCs of 0.940 and 0.895, respectively.This study presents a simple and practical mortality prediction model that performed well for important subgroups of patients as well as for the heterogeneous population of all acute trauma admissions in the Netherlands. Because this model includes widely available predictors, it can also be used for international evaluations of trauma care within institutions and trauma systems.

Background Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not necessarily discriminate between patients who benefit from immediate care at Level-1 trauma centers. The aim of this study was to assess which patients benefit from direct transportation to Level-1 trauma centers. Methods We used the American National Trauma Data Bank (NTDB), a retrospective observational cohort. All adult patients (ISS > 3) between 2015 and 2016 were included. Patients who were self-presenting or had isolated limb injury were excluded. We used logistic regression to assess the association of direct transportation to Level-1 trauma centers with in-hospital mortality adjusted for clinically relevant confounders. We used this model to define benefit as predicted probability of mortality associated with transportation to a non-Level-1 trauma center minus predicted probability associated with transportation to a Level-1 trauma center. We used a threshold of 1% as absolute benefit. Potential interaction terms with transportation to Level-1 trauma centers were included in a penalized logistic regression model to study which patients benefit. Results We included 388,845 trauma patients from 232 Level-1 centers and 429 Level-2/3 centers. A small beneficial effect was found for direct transportation to Level-1 trauma centers (adjusted Odds Ratio: 0.96, 95% Confidence Interval: 0.92–0.99) which disappeared when comparing Level-1 and 2 versus Level-3 trauma centers. In the risk approach, predicted benefit ranged between 0 and 1%. When allowing for interactions, 7% of the patients (n = 27,753) had more than 1% absolute benefit from direct transportation to Level-1 trauma centers. These patients had higher AIS Head and Thorax scores, lower GCS and lower SBP. A quarter of the patients with ISS > 15 were predicted to benefit from transportation to Level-1 centers (n = 26,522, 22%). Conclusions Benefit of transportation to a Level-1 trauma centers is quite heterogeneous across patients and the difference between Level-1 and Level-2 trauma centers is small. In particular, patients with head injury and signs of shock may benefit from care in a Level-1 trauma center. Future prehospital triage models should incorporate more complete risk profiles.

OBJECTIVE: To develop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit.PATIENTS AND METHODS: A risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study.RESULTS: Eleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (C statistic = 0.76; observed 3-year diabetes rate was 1.8% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (HR, 1.1; 95% CI, 0.61 to 2.0 in the lowest-risk quarter vs HR, 0.45; 95% CI, 0.35 to 0.59 in the highest-risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification and 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients.CONCLUSION: The Tufts-Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.

BackgroundAn intensive lifestyle modification program or metformin pharmacotherapy reduced the risk of developing diabetes in patients at high risk, but are not widely used in the 88 million American adults with prediabetes.ObjectiveDevelop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit.DesignCross-design synthesis: risk prediction model developed and validated in large observational database, treatment effect estimates from risk-based reanalysis of clinical trial data.SettingOutpatient clinics in US.PatientsRisk model development cohort: 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); validation cohort: distinct sample of 1.1 million patients in OLDW. Randomized clinical trial cohort: 3081 people from the Diabetes Prevention Program (DPP) study.InterventionsRandomization in the DPP: 1) an intensive program of lifestyle modification; 2) standard lifestyle recommendations plus 850 mg metformin twice daily; or 3) standard lifestyle recommendations plus placebo twice daily.ResultsEleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (c-statistic = 0.76; observed 3-year diabetes rate was 1.8% in lowest-risk quarter and 19.6% in highest-risk quarter). In the DPP, the hazard ratio for lifestyle modification was constant across all levels of risk (HR = 0.43, 95% CI 0.35 – 0.53); while the HR for metformin was highly risk-dependent (HR HR = 1.1 [95% CI: 0.61 - 2.0] in the lowest-risk quarter vs. HR=0.45 [95% CI: 0.35 0.59] in the highest risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification, and 76% of the benefits of population-wide metformin therapy can be obtained targeting the highest risk quarter of patients.LimitationsDifferences in variable definitions and in missingness across observational and trial settings may introduce estimation error in risk-based treatment effects.ConclusionAn EHR-compatible risk model might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.

Background and aimCOVID-19 is putting extraordinary pressure on emergency departments (EDs). To support decision making in the ED, we aimed to develop a simple and valid model for predicting mortality and need for intensive care unit (ICU) admission in suspected COVID-19 patients.MethodsFor model development, we retrospectively collected data of patients who were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020 (first wave of the pandemic). Based on prior literature we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of the need for ICU admission, both within 28 days after hospital admission. We assessed model performance (Area Under the ROC curve (AUC); calibration plots) with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing values.ResultsThe development data included 5,831 patients, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. ICU admission was fully recorded for 2,633 first wave patients in 2 hospitals, with 214 (8%) ICU admissions within 28 days. A simple model – COVID Outcome Prediction in the Emergency Department (COPE) – with age, respiratory rate, C-reactive protein, lactic dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well-calibrated and showed good discrimination in 3,252 second wave patients (AUC in 4 hospitals: 0.82 [0.78; 0.86]; 0.82 [0.74; 0.90]; 0.79 [0.70; 0.88]; 0.83 [0.79; 0.86]). COPE was also able to identify patients at high risk of needing IC in 706 second wave patients with complete information on ICU admission (AUC: 0.84 [0.78; 0.90]; 0.81 [0.66; 0.95]). The models are implemented in web-based and mobile applications.ConclusionCOPE is a simple tool that is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.CONTRIBUTION TO THE LITERATUREWhat is already known on this topicPrediction models have the potential to support decision making about hospital admission of patients presenting to the emergency department with suspected COVID-19Most currently available models that were independently assessed contain a high risk of biasPromising models were developed in different patient selections and included predictors that are not quickly and objective obtainable in emergency departmentsWhat this study addsA simple and objective tool (“COPE”) is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19COPE may support ED physicians to identify high-risk patients – i.e. those at high risk of deterioration and/or death – requiring treatment in the ICU, intermediate-risk patients requiring admission to the clinical ward, and low-risk patients who can potentially be sent home

Background In patients with complex coronary artery disease (CAD), women favored coronary artery bypass grafting surgery (CABG) compared to percutaneous coronary intervention (PCI) at 5 years in the SYNTAX trial, whereas mortality rates after PCI and CABG were not different in men. On the other hand, poor outcomes of women undergoing PCI were not observed in the PRECOMBAT and BEST trials. The long-term optimal revascularization strategy according to gender has not been fully evaluated. Purpose In the SYNTAX Extended Survival (SYNTAXES) study, no significant difference existed in all-cause death between PCI and CABG at 10 years. This study aimed to assess treatment effect of PCI and CABG for 10-year all-cause death according to gender. Methods The SYNTAXES study evaluated vital status up to 10 years in 1,800 patients with de novo three-vessel disease (3VD) and/or left main coronary artery disease (LMCAD) randomized to treatment with CABG or PCI in the SYNTAX trial, and the pre-specified primary endpoint was all-cause death at 10 years. In this prespecified analysis, all-cause death at 10 years according to gender in patients undergoing PCI or CABG was evaluated. Results Of 1800 patients, 402 (22.3%) were women and 1398 (77.7%) were men. In women, the rate of mortality was significantly higher in the PCI arm at 5 years than in the CABG arm (19.3% vs. 10.3%; Log-rank p=0.010, Figure A), but the rates of mortality were not different at 10 years between the PCI and CABG arms (33.0% vs. 32.5%; Log-rank p=0.600, Figure A). In men, the mortality rate tended to be higher in the PCI arm at 10 years than in the CABG arm (27.0% vs. 22.5%; Log-rank p=0.082, Figure B), although the mortality rates were not different at 5 years between the PCI and CABG arms (12.4% vs. 12.3%; Log-rank p=0.957, Figure B). Conclusion The efficacy of CABG observed at 5 years disappeared at 10 years in women, whereas the efficacy of CABG became apparent after 5 years in men. Figure 1 Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Erasmus University Medical Centre, Rotterdam, Netherlands, reference: MEC-2016-716

Aims Adequate risk prediction can optimize the clinical management in adult congenital heart disease (ACHD). We aimed to update and subsequently validate a previously developed ACHD risk prediction model. Methods and results A prediction model was developed in a prospective cohort study including 602 moderately or severely complex ACHD patients, enrolled as outpatients at a tertiary centre in the Netherlands (2011–2013). Multivariable Cox regression was used to develop a model for predicting the 1-year risks of death, heart failure (HF), or arrhythmia (primary endpoint). The Boston ACHD Biobank study, a prospectively enrolled cohort (n = 749) of outpatients who visited a referral centre in Boston (2012–2017), was used for external validation. The primary endpoint occurred in 153 (26%) and 191 (28%) patients in the derivation and validation cohorts over median follow-up of 5.6 and 2.3 years, respectively. The final model included 5 out of 14 pre-specified predictors with the following hazard ratios; New York Heart Association class ≥II: 1.92 [95% confidence interval (CI) 1.28–2.90], cardiac medication 2.52 (95% CI 1.72–3.69), ≥1 reintervention after initial repair: 1.56 (95% CI 1.09–2.22), body mass index: 1.04 (95% CI 1.01–1.07), log2 N-terminal pro B-type natriuretic peptide (pmol/L): 1.48 (95% CI 1.32–1.65). At external validation, the model showed good discrimination (C-statistic 0.79, 95% CI 0.74–0.83) and excellent calibration (calibration-in-the-large = −0.002; calibration slope = 0.99). Conclusion These data support the validity and applicability of a parsimonious ACHD risk model based on five readily available clinical variables to accurately predict the 1-year risk of death, HF, or arrhythmia. This risk tool may help guide appropriate care for moderately or severely complex ACHD.

Background: Longitudinal bleeding risk scores have been validated in patients treated with dual antiplatelet therapy (DAPT) following percutaneous coronary intervention. How these scores apply to the population of patients with acute coronary syndrome (ACS) treated without revascularization remains unknown. The objective was to evaluate and compare the performances of the PRECISE-DAPT, PARIS, and DAPT (bleeding component) bleeding risk scores in the medically managed patients with ACS treated with DAPT. Methods and Results: TRILOGY ACS (Targeted Platelet Inhibition to Clarify the Optimal Strategy to Medically Manage Acute Coronary Syndromes) was a double-blind, placebo-controlled randomized trial conducted from 2008 to 2012 over a median follow-up of 17.0 months in 966 sites (52 countries). High-risk patients with unstable angina or non–ST-segment–elevation myocardial infarction who did not undergo revascularization were randomized to prasugrel or clopidogrel. The PRECISE-DAPT, PARIS, and DAPT (bleeding component) risk scores were applied in the TRILOGY ACS population to evaluate their performance to predict adjudicated non–coronary artery bypass grafting–related GUSTO (Global Use of Strategies to Open Occluded Coronary Arteries) severe/life-threatening/moderate and TIMI (Thrombolysis in Myocardial Infarction) major/minor bleeding with time-dependent c-indices. Among the 9326 participants, median age was 66 years (interquartile range, 59–74 years), and 3650 were females (39.1%). A total of 158 (1.69%) GUSTO severe/life-threatening/moderate and 174 (1.87%) TIMI major/minor non–coronary artery bypass grafting bleeding events occurred. The c-indices (95% CI) of the PRECISE-DAPT, PARIS, and DAPT (bleeding component) scores through 12 months were 0.716 (0.677–0.758), 0.693 (0.658–0.733), and 0.674 (0.637–0.713), respectively, for GUSTO bleeding and 0.624 (0.582–0.666), 0.612 (0.578–0.651), and 0.608 (0.571–0.649), respectively, for TIMI bleeding. There was no significant difference in the c-indices of each score based upon pairwise comparisons. Conclusions: Among medically managed patients with ACS treated with DAPT, the performances of the PRECISE-DAPT, PARIS, and DAPT (bleeding component) scores were reasonable and similar to their performances in the derivation percutaneous coronary intervention populations. Bleeding risk scores may be used to predict longitudinal bleeding risk in patients with ACS treated with DAPT without revascularization and help support shared decision making. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00699998.

Objective: To compare overall survival in patients with esophageal adenocarcinoma who underwent transhiatal esophagectomy (THE) with limited lymphadenectomy or transthoracic esophagectomy (TTE) with extended lymphadenectomy with or without neoadjuvant chemoradiotherapy (nCRT). Background: The application of neoadjuvant therapy might change the association between the extent of lymphadenectomy and survival in patients with esophageal adenocarcinoma. This may influence the choice of surgical approach in patients treated with nCRT. Methods: Patients with potentially curable subcarinal esophageal adenocarcinoma treated with surgery alone or nCRT followed by surgery in 7 centers were included. The effect of surgical approach on overall survival, differentiated by the addition or omission of nCRT, was analyzed using a multivariable Cox regression model that included well-known prognostic factors and factors that might have influenced the choice of surgical approach. Results: In total, 701 patients were included, of whom 318 had TTE with extended lymphadenectomy and 383 had THE with limited lymphadenectomy. TTE had differential effects on survival (P for interaction = 0.02), with a more favorable prognostic effect in patients who were treated with surgery alone [hazard ratio (HR) = 0.77, 95% confidence interval (CI) 0.58-1.03]. This association was statistically significant in a subgroup of patients with 1 to 8 positive lymph nodes in the resection specimen (HR = 0.62, 95% CI 0.43-0.90). The favorable prognostic effect of TTE over THE was absent in the nCRT and surgery group (HR = 1.16, 95% CI 0.80-1.66) and in the subgroup of nCRT patients with 1 to 8 positive lymph nodes in the resection specimen (HR = 1.00, 95% CI 0.61-1.68). Conclusions: Compared to surgery alone, the addition of nCRT may reduce the need for TTE with extended lymphadenectomy to improve long-term survival in patients with esophageal adenocarcinoma.

Background Dual antiplatelet therapy (DAPT) with aspirin plus a P2Y12 inhibitor prevents ischaemic events after coronary stenting, but increases bleeding. Guidelines support weighting bleeding risk before the selection of treatment duration, but no standardised tool exists for this purpose. Methods A total of 14 963 patients treated with DAPT after coronary stenting—largely consisting of aspirin and clopidogrel and without indication to oral anticoagulation—were pooled at a single-patient level from eight multicentre randomised clinical trials with independent adjudication of events. Using Cox proportional hazards regression, we identified predictors of out-of-hospital Thrombosis in Myocardial Infarction (TIMI) major or minor bleeding stratified by trial, and developed a numerical bleeding risk score. The predictive performance of the novel score was assessed in the derivation cohort and validated in patients treated with percutaneous coronary intervention from the PLATelet inhibition and patient Outcomes (PLATO) trial (n=8595) and BernPCI registry (n=6172). The novel score was assessed within patients randomised to different DAPT durations (n=10 081) to identify the effect on bleeding and ischaemia of a long (12–24 months) or short (3–6 months) treatment in relation to baseline bleeding risk. Findings The PRECISE-DAPT score (age, creatinine clearance, haemoglobin, white-blood-cell count, and previous spontaneous bleeding) showed a c-index for out-of-hospital TIMI major or minor bleeding of 0·73 (95% CI 0·61–0·85) in the derivation cohort, and 0·70 (0·65–0·74) in the PLATO trial validation cohort and 0·66 (0·61–0·71) in the BernPCI registry validation cohort. A longer DAPT duration significantly increased bleeding in patients at high risk (score ≥25), but not in those with lower risk profiles (pinteraction=0·007), and exerted a significant ischaemic benefit only in this latter group. Interpretation The PRECISE-DAPT score is a simple five-item risk score, which provides a standardised tool for the prediction of out-of-hospital bleeding during DAPT. In the context of a comprehensive clinical evaluation process, this tool can support clinical decision making for treatment duration. Funding None.

Background: In 1 of 3 patients with initial lymph node-positive (cN(+)) breast cancer, neoadjuvant chemotherapy (NAC) results in an axillary pathologic complete response (ax-pCR). This urges the need for a less-invasive axillary staging method. Recently introduced less-invasive procedures have been insufficient in accurately identifying ax-pCR. Therefore, we propose a novel less-invasive axillary staging procedure: the Radioactive Iodine Seed localization in the Axilla with the Sentinel node procedure (RISAS), a combination of the procedure of marking axillary lymph nodes with radioactive iodine seeds (MARI) and sentinel lymph node biopsy (SLNB). Patients and Methods: In the present open single-arm multicenter validation study, 225 cN(+) (biopsy-proven) patients will undergo the RISAS procedure, in which a positive lymph node is marked by an iodine-125 seed before NAC. After NAC completion, this iodine-125 seed-marked lymph node is removed, together with any additional sentinel lymph nodes. The RISAS procedure is subsequently followed by completion axillary lymph node dissection (ALND). The RISAS lymph nodes will be compared with the lymph nodes from the completion ALND specimen. The primary endpoint is accuracy of the RISAS procedure. The identification rate, false-negative rate, negative predictive value, and possible concordance between the MARI and SLNB will be reported. Conclusion: The present prospective multicenter RISAS trial will enable us to validate the combination of MARI and SLNB for assessing the axillary response to NAC in cN(+) patients. If RISAS proves to be an accurate axillary staging procedure, ALND could safely be abandoned in the case of ax-pCR confirmed using the RISAS procedure.

OBJECTIVES: The study aimed to (i) determine the influence of poor health on competing exit routes from paid employment among older workers in Europe, (ii) assess whether these risks are different among welfare state regimes in Europe, and (iii) evaluate differences in estimates between two different competing risk approaches. METHODS: The study population consisted of 5273 respondents (6-years follow-up) from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The effect of poor health on exit routes from paid employment was assessed with a cause-specific Cox model and a Fine & Gray (F&G) model. These two competing risk analyses were used to calculate absolute risks of labor force exit among welfare state regimes in Europe. RESULTS: In both models, poor health was a risk factor for disability benefit [hazard ratio (HR) 3.36; subdistribution hazard ratio (SHR) 3.22], and unemployment (HR 1.43, SHR 1.32). Both models produced similar absolute risks. In countries with a Bismarckian welfare state regime, low-educated older workers living alone and in poor health had an 11% risk of disability benefit, 7% of unemployment, 46% of early retirement, and 7% of becoming economically inactive. In countries with a Scandinavian welfare state regime, the risks were 10%, 7%, 29%, and 3%, respectively, and in Southern European welfare state regimes 4%, 5%, 35%, and 7%. CONCLUSIONS: Workers with poor health are more likely to leave the labor force than workers with good health. The absolute risks of early retirement and becoming economically inactive were lowest in countries with a Scandinavian welfare state regime. For disability benefit and unemployment, absolute risks were lowest in Southern European welfare state regimes. The direct estimation of absolute risks of leaving the labor force in the presence of competing exit routes is an appealing feature of the F&G model.

Background. The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction. Methods. To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables. Results. Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively). Conclusions. Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost-effectiveness and misallocate resources.

The originally-proposed PRECISE-DAPT score is a 5-item risk score supporting decision-making for dual antiplatelet therapy1 duration after PCI. It is unknown if a simplified version of the score based on 4 factors (age, hemoglobin, creatinine clearance, prior bleeding), and lacking white-blood cell count, retains potential to guide DAPT duration. The 4-item PRECISE-DAPT was used to categorize 10,081 patients who were randomized to short (3-6 months) or long (12-24 months) DAPT regimen according to high (HBR defined by PRECISE-DAPT ≥25 points) or non-high bleeding risk (PRECISE-DAPT

Objective Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting We illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random-effects meta-analysis methods. I 2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation. Results Estimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I 2 statistics and prediction intervals for c-statistics. Conclusion This study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods.

Objectives The study sought to validate the SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) score II mortality prediction model after percutaneous coronary intervention (PCI) or coronary artery bypass grafting in a large pooled population of patients with multivessel coronary disease (MVD) and/or unprotected left main disease (UPLMD) enrolled in the PRECOMBAT (Bypass Surgery Versus Angioplasty Using Sirolimus-Eluting Stent in Patients With Left Main Coronary Artery Disease) and BEST (Artery Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease) randomized controlled trials. Background For patients with MVD and/or UPLMD, the choice of the best revascularization strategy remains challenging. Methods Pooled individual patient-level data from PRECOMBAT and BEST were used to assess calibration and discrimination of the SYNTAX score II prediction model for all-cause mortality after PCI and coronary artery bypass grafting at 4-year follow-up. The study population comprised 1,480 patients (600 with UPLMD, 880 with MVD). Results The overall incidence of all-cause mortality was 6.1% after a median follow-up period of 4.9 years. Validation plots showed good model calibration overall and across treatment groups but tended to overestimate all-cause mortality in the highest risk quintiles of patients in the whole population and the PCI arm. The SYNTAX score II showed moderate discrimination ability for the whole population (C index = 0.685) but better for patients receiving PCI than CABG (C index = 0.718 vs. 0.662 in patients with UPLMD, C index = 0.700 vs. 0.661 in those with MVD). Observed all-cause mortality was higher when the treatment received was at variance with that recommended by the model and similar when it was concordant. Conclusions The SYNTAX score II has good calibration but only moderate discrimination ability for long-term mortality prediction in this randomized population. This score provides an important tool to help guide the heart team’s decision-making process regarding the selection of the best revascularization strategy for patients with MVD and/or UPLMD. (Bypass Surgery Versus Angioplasty Using Sirolimus-Eluting Stent in Patients With Left Main Coronary Artery Disease, NCT00422968; Bypass Surgery Versus Everolimus-Eluting Stent Implantation for Multivessel Coronary Artery Disease, NCT00997828)

Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression coefficient validity on discriminative ability. We derived variance formulas for both mbc and c-mbc. We compared the mbc and the c-mbc with commonly used concordance measures in a simulation study and in two external validation settings. The mbc was asymptotically equivalent to a previously proposed resampling-based case-mix corrected c-index. The c-mbc remained stable at the true value with increasing proportions of censoring, while Harrell's c-index and to a lesser extent Uno's concordance measure increased unfavorably. Variance estimates of mbc and c-mbc were well in agreement with the simulated empirical variances. We conclude that the mbc is an attractive closed-form measure that allows for a straightforward quantification of the expected change in a model's discriminative ability due to case-mix heterogeneity. The c-mbc also reflects regression coefficient validity and is a censoring-robust alternative for the c-index when the proportional hazards assumption holds. Copyright © 2016 John Wiley & Sons, Ltd.

The performance of previously developed models to predict non sentinel lymph node status for patients with sentinel lymph node-positive breast cancer was poor for our Dutch patient population. Therefore, a new model was developed that includes only 2 predictors, is easy to use, and can help clinicians and their patients in the clinical decision-making regarding axillary treatment. Background: Multiple predictive systems have previously been developed to identify the sentinel lymph node (SLN)-positive patients at low risk of additional axillary non-SLN involvement and for whom completion axillary lymph node dissection (ALND) could be avoided. However, previous studies showed that these tools had poor performance in Dutch patients with breast cancer, probably owing to variations in pathology settings and differences in population characteristics. The aim of the present study was to develop a predictive tool for the risk of non-SLN involvement in a Dutch population with SLN-positive breast cancer. Materials and Methods: The data from 513 patients with SLN-positive breast cancer at 10 participating hospitals, who had undergone ALND from January 2007 to December 2008 were studied. The uni- and multivariable associations of predictors for non-SLN metastases were analyzed, and a predictive model was developed. The discriminatory ability of the model was measured by the area under the receiver operating characteristic curve (AUC) and the agreement between predicted probabilities and observed frequencies was visualized by a calibration plot. Results: A predictive model was developed that included the 2 strongest predictors: the size of the SLN metastases in millimeters and the presence of a negative sentinel lymph node. The model showed good discriminative ability (AUC, 0.75) and good calibration over the complete range of predicted probabilities. Conclusion: We have developed a tool to predict additional non-SLN metastases in Dutch patients with SLN-positive breast cancer that is easy to use in daily clinical breast cancer practice. (C) 2016 Elsevier Inc. All rights reserved.

Objectives To develop prediction models for Chlamydia trachomatis (Ct) infection with different levels of detail in information, that is, from readily available data in registries and from additional questionnaires. Methods All inhabitants of Rotterdam and Amsterdam aged 16–29 were invited yearly from 2008 until 2011 for home-based testing. Their registry data included gender, age, ethnicity and neighbourhood-level socioeconomic status (SES). Participants were asked to fill in a questionnaire on education, sexually transmitted infection history, symptoms, partner information and sexual behaviour. We developed prediction models for Ct infection using first-time participant data—including registry variables only and with additional questionnaire variables—by multilevel logistic regression analysis to account for clustering within neighbourhoods. We assessed the discriminative ability by the area under the receiver operating characteristic curve (AUC). Results Four per cent (3540/80 385) of the participants was infected. The strongest registry predictors for Ct infection were young age (especially for women) and Surinamese, Antillean or sub-Saharan African ethnicity. Neighbourhood-level SES was of minor importance. Strong questionnaire predictors were low to intermediate education level, ethnicity of the partner (non-Dutch) and having sex with casual partners. When using a prediction model including questionnaire risk factors (AUC 0.74, 95% CI 0.736 to 0.752) for selective screening, 48% of the participating population needed to be screened to find 80% (95% CI 78.4% to 81.0%) of Ct infections. The model with registry risk factors only (AUC 0.67, 95% CI 0.656 to 0.675) required 60% to be screened to find 78% (95% CI 76.6% to 79.4%) of Ct infections. Conclusions A registry-based prediction model can facilitate selective Ct screening at population level, with further refinement at the individual level by including questionnaire risk factors.

Objectives: We aimed to compare the performance of different regression modeling approaches for the prediction of heterogeneous treatment effects. Study Design and Setting: We simulated trial samples (n = 3,600; 80% power for a treatment odds ratio of 0.8) from a superpopulation (N = 1,000,000) with 12 binary risk predictors, both without and with six true treatment interactions. We assessed predictions of treatment benefit for four regression models: a “risk model” (with a constant effect of treatment assignment) and three “effect models” (including interactions of risk predictors with treatment assignment). Three novel performance measures were evaluated: calibration for benefit (i.e., observed vs. predicted risk difference in treated vs. untreated), discrimination for benefit, and prediction error for benefit. Results: The risk modeling approach was well-calibrated for benefit, whereas effect models were consistently overfit, even with doubled sample sizes. Penalized regression reduced miscalibration of the effect models considerably. In terms of discrimination and prediction error, the risk modeling approach was superior in the absence of true treatment effect interactions, whereas penalized regression was optimal in the presence of true treatment interactions. Conclusion: A risk modeling approach yields models consistently well calibrated for benefit. Effect modeling may improve discrimination for benefit in the presence of true interactions but is prone to overfitting. Hence, effect models—including only plausible interactions—should be fitted using penalized regression.

Introduction: Most heart failure (HF) clinical prediction models (CPMs] have not been independently externally validated. We sought to test the performance of HF models in a diverse population using a systematic approach. Methods: A systematic review identified CPMs predicting outcomes for patients with HF. Individual patient data from 5 large publicaly available clinical trials enrolling patients with chronic HF were matched to published CPMs based on similarity in populations and available outcome and predictor variables in the clinical trial databases. CPM performance was evaluated for discrimination (c-statistic, % relative change in c-statistic) and calibration (Harrell’s E and E 90 , the mean and the 90% quantile of the error distribution from the smoothed loess observed value) for the original and recalibrated models. Results: Out of 135 HF CPMs reviewed, we identified 45 CPM-trial pairs including 13 unique CPMs. The outcome was mortality for all of the models with a trial match. During external validations, median c-statistic was 0.595 (IQR 0.563 to 0.630) with a median relative decrease in the c-statistic of -57 % (IQR, -49% to -71%) compared to the c-statistic reported in the derivation cohort. Overall, the median Harrell’s E was 0.09 (IQR, 0.04 to 0.135) and E 90 was 0.11 (IQR, 0.07 to 0.21). Recalibration of the intercept and slope led to substantially improved calibration with median change in Harrell’s E of -35% [IQR 0 to -75%] for the intercept and -56% [IQR -17% to -75%] for the intercept and slope. Refitting model covariates improved the median c-statistic by 38% to 0.629 [IQR 0.613 to 0.649]. Conclusion: For HF CPMs, independent external validations demonstrate that CPMs perform significantly worse than originally presented; however with significant heterogeneity. Recalibration of the intercept and slope improved model calibration. These results underscore the need to carefully consider the derivation cohort characteristics when using published CPMs.

Aims: The aim of the study was to evaluate the effect of strut protrusion (SP) on wall shear stress (WSS) and neointimal growth (NG) one and five years after implantation of an Absorb bioresorbable vascular scaffold. Methods and results: Eight patients were selected from a first-in-man study. Following three-dimensional (3D) reconstruction of coronaries, WSS was quantified using Newtonian steady-flow simulation in each cross-section at 5° subunits (sectors) of the circumferential luminal surface. At one year, neointimal thickness (NT) was measured by optical coherence tomography (OCT) and correlated to WSS and SP post procedure. Median SP was 112.9 (90.8, 133.1) µm post implantation. Post procedure, a logarithmic inverse relationship between SP and post-implantation WSS (r=–0.425, p

We welcome the comments by Dr. Montalto and colleagues regarding our recent paper in the Journal [(1)][1], suggesting that PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy) should be validated and recalibrated in

Objectives: To assess the clinical profile and long-term mortality in SYNTAX score II based strata of patients who received percutaneous coronary interventions (PCI) in contemporary randomized trials. Background: The SYNTAX score II was developed in the randomized, all-comers' SYNTAX trial population and is composed by 2 anatomical and 6 clinical variables. The interaction of these variables with the treatment provides individual long-term mortality predictions if a patient undergoes coronary artery bypass grafting (CABG) or PCI. Methods: Patient-level (n = 5433) data from 7 contemporary coronary drug-eluting stent (DES) trials were pooled. The mortality for CABG or PCI was estimated for every patient. The difference in mortality estimates for these two revascularization strategies was used to divide the patients into three groups of theoretical treatment recommendations: PCI, CABG or PCI/CABG (the latter means equipoise between CABG and PCI for long term mortality). Results: The three groups hadmarked differences in their baseline characteristics. According to the predicted risk differences, 5115 patients could be treated either by PCI or CABG, 271 should be treated only by PCI and, rarely, CABG (n = 47) was recommended. At 3-year follow-up, according to the SYNTAX score II recommendations, patients recommended for CABG had higher mortality compared to the PCI and PCI/CABG groups (17.4%; 6.1% and 5.3%, respectively; P < 0.01). Conclusions: The SYNTAX score II demonstrated capability to help in stratifying PCI procedures. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

Background Cigarette smoking is a well-known risk factor for development of coronary artery disease (CAD). However, some studies have suggested a "smoker's paradox," meaning neutral or favorable outcomes in smokers who have developed CAD, especially myocardial infarction (MI). Objectives The study aimed to examine the association of smoking status with clinical outcomes in the randomized controlled SYNTAX (SYNergy Between PCI With TAXUS and Cardiac Surgery) trial at 5-year follow-up. Methods Detailed smoking history was collected at baseline, 6-month, 1-year, 3-year, and 5-year follow-up. The composite endpoints included death/MI/stroke (primary endpoint) plus major adverse cardiac and cerebrovascular events (MACCE) (combination of death/MI/stroke and target lesion revascularization) according to patient smoking status. The comparison of 5-year clinical outcomes between the groups according to smoking status was performed with Cox regression using smoking status at baseline or smoking as a time-dependent covariate. Results A sizeable proportion (n = 322, 17.9%) of patients had changing smoking status during 5-year follow-up. One in 5 patients with complex CAD was smoking at baseline. However, 60% stopped after revascularization while others continued to smoke. Smokers had worse clinical outcomes due to a higher incidence of recurrent MI in both revascularization arms. Smoking was an independent predictor of the composite endpoint of death/MI/stroke (hazard ratio [HR]: 1.8; 95% confidence interval [CI]: 1.3 to 2.5; p = 0.001) and MACCE (HR: 1.4; 95% CI: 1.1 to 1.7; p = 0.02). Conclusions Smoking is associated with poor clinical outcomes after revascularization in patients with complex CAD. This places further emphasis on efforts at smoking cessation to improve revascularization benefits. (SYNTAX Study: TAXUS Drug-Eluting Stent Versus Coronary Artery Bypass Surgery for the Treatment of Narrowed Arteries; NCT00114972)

Objectives: We aimed to compare modeling approaches to estimate the individual survival benefit of treatment with either coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) for patients with complex coronary artery disease. Study Design and Setting: We estimated survival with Cox regression models that included the treatment variable (CABG/PCI) interacting with either an internally developed overall prognostic index (PI) or with individual prognostic factors. We analyzed data of patients who were randomized in the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery trial (1,800 patients, 178 deaths). Results: A negligible interaction with the PI (P = 0.51) led to 4-year survival estimates in favor of CABG for all patients. In contrast, individual interactions indicated substantial relative treatment effect heterogeneity (overall interaction P = 0.004), and estimates of 4-year survival were numerically in favor of CABG for 1,275 of 1,800 patients (71%; 519 with 95% confidence). To test the more complex model with individual interactions, we first used penalized regression, resulting in smaller but largely Consistent individual estimates of the survival difference between CABG and PCI. Second, strong treatment interactions were confirmed at external validation in 2,891 patients from a multinational registry. Conclusion: Modeling strategies that omit interactions may result in misleading estimates of absolute treatment benefit for individual patients with the potential hazard of suboptimal decision making. (C) 2015 Elsevier Inc. All rights reserved.

Knowledge about lower leg chronic exertional compartment syndrome (CECS) is largely obtained from highly selected populations. Patient characteristics may therefore not be appropriate for the general population. Our purpose was to describe a heterogeneous population of individuals suspected of lower leg CECS and to identify predictors of CECS. Charts of individuals who were analyzed for exercise-induced lower leg pain in a referral center between 2001 and 2013 were retrospectively studied. Patients were included if history and physical examination were suggestive of CECS and if they had undergone a dynamic intracompartmental pressure measurement. Six hundred ninety-eight of 1411 individuals were diagnosed with CECS in one or more of three lower leg muscle compartments (anterior tibial, deep flexor, lateral). Prevalence of CECS peaked around the age of 20–25 years and decreased thereafter, although a plateau around 50 years was found. Age, gender, bilateral symptoms, previous lower leg pathology, sports (running and skating) and tender muscle compartments were identified as independent predictors of lower leg CECS. The proposed predictive model has moderate discriminative ability (AUC 0.66) and good calibration over the complete range of predicted probabilities. The predictive model, displayed as a nomogram, may aid in selecting individuals requiring an invasive dynamic intracompartmental muscle pressure measurement.

BACKGROUND: Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models.METHODS: We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients.RESULTS: The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03).CONCLUSIONS: This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods.

Background The results of SYNTAX trial have been reported based on "corelab" calculated SS (cSS). It has been shown that reproducibility of SS is better among the core laboratory technicians than interventional cardiologists. Thus, the prognostic value and clinical implication of the "site" SYNTAX SS (sSS) remain unknown. Objectives The study sought to evaluate the prognostic value and clinical implication of the sSS after percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) surgery in the randomized SYNTAX trial. Methods The sSS was calculated by the site investigators before randomization in the SYNTAX trial. New tertiles based on the sSS were defined with low (0 to 19), intermediate (20 to 27), and high (≥28) scores. The clinical endpoints were compared between PCI and CABG by Kaplan-Meier estimates, log-rank comparison, and Cox regression analyses using the new tertiles. The sSS-based SS II was calculated and its predictive performance was evaluated. Results The mean difference in cSS and sSS is 3.8 ± 11.2, with a mean absolute difference of 8.9 ± 7.8. In the overall cohort, using sSS there was a higher incidence of major adverse cardiac and cerebrovascular events (MACCE) at 5-year follow-up in the PCI group for low (31.9% vs. 24.5%; p = 0.054), intermediate (39.5% vs. 29.5%; p = 0.019), and high (43.0% vs. 31.4%; p = 0.003) tertiles, compared with the CABG group. Similarly, in the 3-vessel disease subgroup, 5-year MACCE rates were higher in PCI group in all tertiles. Conversely, in the left main subgroup, MACCE rates were similar for PCI and CABG groups in all tertiles. The sSS-based SS II (c-index: 0.736) had predictive performance similar to the cSS-based SS II (c-index: 0.744), with net reclassification index of -0.0062 (p = 0.79). Conclusions Appropriate training and unbiased assessment are needed when using SS in clinical decision making. sSS and tertiles based on sSS showed poor discrimination among low, intermediate, and high-risk groups. However, combining clinical factors with sSS retained the predictive performance of SS II. (SYNTAX Study: TAXUS Drug-Eluting Stent Versus Coronary Artery Bypass Surgery for the Treatment of Narrowed Arteries; NCT00114972)

Models to predict long-term ischemic and bleeding risk in patients treated with drug eluting stent were developed from the PARIS registry. The discrimination of the scores were moderate with adequate calibration in the ADAPT-DES registry. We aimed to externally validate PARIS score in all-comers PCI

In patients with acute coronary syndromes (ACS) on dual anti-platelet therapy (DAPT) and managed medically, we compared the performance of bleeding risk prediction models originally developed for percutaneous coronary intervention (PCI) patients. The PRECISE-DAPT, PARIS, and DAPT (bleeding

Objective B-mode ultrasound measurement of the carotid intima-media thickness (CIMT) is a widely used marker for atherosclerosis and is associated with future cardiovascular events. This article provides a review and meta-analysis of the published evidence on the association of CIMT with future cardiovascular events and its additional value to traditional cardiovascular risk prediction models. Methods A systematic review and meta-analysis of the evidence on the association of CIMT with future cardiovascular events and the additional value of CIMT to traditional cardiovascular risk prediction models was conducted. The association of CIMT with future cardiovascular events and the additional value of CIMT were calculated using random effects analysis. Results The literature search yielded 1196 articles of which 15 articles provided sufficient data for the meta-analysis. A 1 SD increase in CIMT was predictive for myocardial infarction (HR 1.26, 95% CI 1.20–1.31) and for stroke (HR 1.31, 95% CI 1.26–1.36). A 0.1 mm increase in CIMT was predictive for myocardial infarction (HR 1.15, 95% CI 1.12–1.18) and for stroke (HR 1.17, 95% CI 1.15–1.21). The overall performance of risk prediction models did not significantly increase after addition of CIMT data. The areas under the curve increased from 0.726 to 0.729 ( p = 0.8). Conclusions CIMT as measured by B-mode ultrasound is associated with future cardiovascular events. However, the addition of CIMT to traditional cardiovascular risk prediction models does not lead to a statistical significantly increase in performance of those models.